U.S. patent application number 11/628611 was filed with the patent office on 2007-09-27 for multispectral scanner with enlarged gamut, in particular a single-pass flat-bed scanner.
Invention is credited to Pascal Cotte, Damien Dupraz.
Application Number | 20070223058 11/628611 |
Document ID | / |
Family ID | 34945163 |
Filed Date | 2007-09-27 |
United States Patent
Application |
20070223058 |
Kind Code |
A1 |
Cotte; Pascal ; et
al. |
September 27, 2007 |
Multispectral Scanner With Enlarged Gamut, in Particular a
Single-Pass Flat-Bed Scanner
Abstract
The scanner comprises an integrated photosensitive linear sensor
(20) comprising N parallel lines of photosites, where N.gtoreq.4,
and preferably N.gtoreq.6, with each line of photosites being
associated with a respective bandpass optical filter. For each
scanning step and for each pixel of the analyzed line, it delivers
N corresponding quantized partial measurement values, each
representative of the spectral reflectance of the document sensed
through respective ones of the N filters. Spectral reconstruction
means operate using an extrapolation method based on training from
reference color samples, having a memory (42) storing a knowledge
base formed from known spectral reflectance values of said
reference samples, and a neural network (40) receiving as inputs
the N quantized partial values and delivering as output at least
one reconstituted quantized value representative of the spectral
reflectance of the corresponding pixel of the document.
Inventors: |
Cotte; Pascal; (Tilly,
FR) ; Dupraz; Damien; (Sartrouville, FR) |
Correspondence
Address: |
NIXON & VANDERHYE, PC
901 NORTH GLEBE ROAD, 11TH FLOOR
ARLINGTON
VA
22203
US
|
Family ID: |
34945163 |
Appl. No.: |
11/628611 |
Filed: |
May 30, 2005 |
PCT Filed: |
May 30, 2005 |
PCT NO: |
PCT/FR05/01322 |
371 Date: |
April 25, 2007 |
Current U.S.
Class: |
358/474 |
Current CPC
Class: |
G01J 3/462 20130101;
G01J 3/46 20130101; G01J 3/524 20130101; G01J 3/51 20130101; H04N
1/193 20130101; H04N 1/1013 20130101; G01J 3/513 20130101; H04N
1/1043 20130101 |
Class at
Publication: |
358/474 |
International
Class: |
H04N 1/193 20060101
H04N001/193 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 7, 2004 |
FR |
0406098 |
Claims
1. A multispectral scanner comprising: a photosensitive linear
sensor (20) suitable for analyzing a line of a document in a
transverse direction; a set of N bandpass optical filters (51-56)
where N.gtoreq.4; lighting means (18) suitable for forming an
illuminated strip on the document in the region analyzed by the
sensor; and motor means suitable for causing the document to be
scanned in controlled manner in successive steps in a longitudinal
direction; the scanner being suitable for delivering, for each
scanning step and for each pixel of the analyzed line, N
corresponding quantized partial measurement values, each
representative of the spectral reflectance of the document sensed
by the sensor through a respective one of the N filters; the
scanner being characterized: in that spectral reconstruction means
are provided for spectrally reconstructing the image of the
document and operating using an extrapolation method based on
training with reference color samples, said means comprising: a
memory (42) storing a knowledge base made from known spectral
reflectance values for said reference samples; and a neural network
(40) receiving as its inputs, for each pixel, said N quantized
partial measurement values, and outputting at least one
reconstituted quantized value representative of the spectral
reflectance of the corresponding pixel of the document.
2. The scanner of claim 1, in which: the sensor (20) is an
integrated component having N parallel lines of photosites, with
each line of photosites being associated with a respective one of
the N bandpass optical filters; and said scanning over the extent
of the document is scanning performed in a single pass.
3. The scanner of claim 1, in which N.gtoreq.6, preferably N=6.
4. The scanner of claim 2, of the flat-bed scanner type having an
exposure window (14) for receiving the document go be scanned.
5. The scanner of claim 1, in which the neural network (40) is a
network having multiple thresholds, suitable for receiving as
inputs the N measurement values, for applying weightings specific
to the N values, and for outputting a plurality of individual
reconstituted quantized values associated with corresponding
spectral components of the reflectance of the pixel.
6. The scanner of claim 5, in which the neural network outputs a
number N' of individual reconstituted quantized values that is
greater than the number N of measurement values.
7. The scanner of claim 5, in which the number N' of individual
reconstituted quantized values is at least 15 values, preferably at
least 25 values, more preferably 30 values, for a number N of
measurement values equal to 6 or to 7.
8. The scanner of claim 1, in which said spectral reconstruction
means for reconstructing the image of the document comprise means
for applying bootstrap type iterative resampling processing to the
N measurement values prior to applying said N measurement values to
the inputs of the neural network.
Description
[0001] The invention relates to the field of calorimetric
analysis.
[0002] The sensation of color results from perceiving radiation
covering a given set of wavelengths.
[0003] A color is characterized by a parameter known as "spectral
reflectance" which describes in the form of a continuous
characteristic (spectrum) the distribution of the proportions of
the different wavelengths over the extent of the visible range.
[0004] This spectral reflectance can be determined directly by a
spectrophotometer or by a spectroradiometer, which are instruments
provided with a dispersive system such as Newton's prism, enabling
a selective band of wavelengths to be projected onto a sensor.
Nevertheless, those instruments are complex and difficult to use,
which means they are restricted to laboratory and metrology
applications.
[0005] In conventional digital imaging, colors are usually analyzed
using three color filters: red, green, and blue (RGB three-color
selection). In order to refine color description and
discrimination, it is also possible to perform multispectral
acquisition, e.g. using six filters, thereby doubling the number of
digital values that are acquired. The color information that
results from such analysis can be described and stored in the form
of three (or six) coordinates defined in the CIE colorimetry
system, and represented relative to the CIE chromaticity diagram in
a two-dimensional space.
[0006] It is also possible to use a calorimeter, which is a
measuring instrument provided with a sensor, a light source, and a
set of filters, generally four filters, enabling a standardized CIE
pair to be produced comprising a standardized illuminant and a
standard observer. For a given CIE illuminant, the calorimeter
serves to obtain coordinates in a color space of the CIE L*u*v, CIE
L*a*b, XYZ, etc. type, with colorimetric systems themselves being
well known and abundantly referenced.
[0007] For more ample information on this topic, reference can be
made in particular to Acquisition and reproduction of color images:
calorimetric and multispectral approaches by J. Y. Harderberg, PhD
dissertation, Ecole nationale superieure des telecommunications,
Paris, France, 1999, or to Physique de coleur: de l'apparence
coloree a la technique colorimetrique [The physics of color: from
colored appearance to colorimetry technique] by R. Seve, Masson,
France, 1996.
[0008] A color acquisition system using RGB filters or a
calorimeter nevertheless provides only discrete color coordinate
values, three values or six values depending on the number of
filters, and does not provide a continuous reflectance spectrum
which is the only way of representing the physical reality behind
the perception of color.
[0009] Knowledge of only three or six color coordinates does not
make it possible to obtain perfect characterization of a given
color. Various methods (explained below) have been proposed for
reconstituting a spectral reflectance characteristic from color
coordinates; for example the so-called "interpolation" method
enables spectral reflectance to be approximated on 30 points on the
basis of knowledge of only six color coordinates.
[0010] Nevertheless, the reconstruction algorithms that have been
implemented until now do not make it possible starting from only
six color coordinates (and a fortiori from only three color
coordinates) to reconstruct certain spectra that correspond to
subtle shades of color, some of which are in widespread use in
painting: it is thus not possible to find the subtle shades of a
"cobalt blue", an "aureolin yellow", a "smaragdine green" or a
"celadon green", an "andrinople red", an "eburnine white", etc.
which are replaced by colors that approximate thereto.
[0011] In order to increase the fidelity which colors are
reproduced, in particular when performing very high resolution and
very high fidelity digitizing of collections held by museums,
proposals have been made to further increase the amount of color
information, e.g. by subdividing the light spectrum using thirteen
filters mounted in a filter-carrier turret, as can be done with the
"Jumboscan" camera developed by the supplier Lumiere Technology
SA.
[0012] Nevertheless, that constitutes an installation which
although capable of high fidelity reproduction, is expensive and
complex to implement: careful preparation of filters having the
desired characteristics (the filters are interference filters
having very narrow passbands); increasing numbers of analysis
passes (as many passes as there are different filters); pass
reproducibility requiring a mechanical system that is extremely
accurate (the thirteen scanned images must be superposable, pixel
on pixel, to within one micrometer); correction of chromatic
aberrations in the optical system, etc. This means that its use is
restricted to special applications, essentially in the field of
museography.
[0013] As a result there exists a considerable need that has yet to
be satisfied for a scanner that is simple and efficient in
structure, and thus capable of being made at low manufacturing
cost, and that enables very high fidelity colorimetric analysis to
be undertaken of a document, with the analysis being effective over
the entire visible color space, giving the possibility of
distinguishing very subtle shades of color.
[0014] To this end, the invention provides a multispectral scanner
of known type, e.g. as disclosed in above-mentioned WO-A-00/25509,
i.e. comprising: a linear photosensitive sensor suitable for
analyzing a line of a document in a transverse direction; a set of
N bandpass optical filters, with N.gtoreq.4, preferably N.gtoreq.6;
lighting means suitable for forming an illuminated strip on the
document in the region being analyzed by the sensor; and motor
means suitable for driving a controlled scan of the document in
successive steps in a longitudinal direction. For each scanning
step and for each pixel of the line under analysis, the scanner is
suitable for delivering N corresponding quantized partial
measurement values, each representative of the spectral reflectance
of the document as sensed by the sensor through respective ones of
the N filters.
[0015] In a first aspect of the invention, spectral reconstruction
means are provided for spectrally reconstructing the image of the
document and operating using an extrapolation method based on
training with reference color samples, said means comprising: a
memory storing a knowledge base made from known spectral
reflectance values for said reference samples; and a neural network
receiving as its inputs, for each pixel, said N quantized partial
measurement values, and outputting at least one reconstituted
quantized value representative of the spectral reflectance of the
corresponding pixel of the document.
[0016] In a second aspect of the invention, means are provided for
applying bootstrap type iterative resampling processing to the N
measurement values before the N measurement values are applied to
the inputs of the neural network.
[0017] As explained in the description below, the invention can be
implemented using a conventional RGB scanner mechanism, e.g. a
conventional A3 or A4 format office flat-bed scanner.
[0018] The sensor is preferably an integrated component having N
parallel lines of photosites, with each line of photosites being
associated with a respective one of the N bandpass optical filters,
and with the entire document being scanned in a single pass.
Analyzing the document in a single pass serves in particular to
avoid any need to have recourse to precision mechanical systems for
scanning of the kind used in prior art systems where it is
necessary to ensure that multiple passes coincide reproducibly.
[0019] The above configuration of the invention can be applied most
advantageously to a flat-bed type scanner having an exposure window
for receiving the document to be scanned.
[0020] Unlike conventional RGB scanners having a "gamut", i.e. a
sensed color range that occupies only 50% to 70% of the spectrum, a
multispectral flat-bed scanner suitable for covering 100% of the
visible color spectrum presents a considerable advantage in a very
large number of industrial and artistic applications, including the
following: [0021] the "packaging" and advertising field, where
colors are usually defined on the basis of four, five, or six
colors, where the additional reference colors include specific
Pantone (registered trademark) colors which in 60% of cases lie
outside the gamut of RGB scanners; the ability to recognize a
Pantone color in an image by means of a device that is as simple to
use as an office scanner constitutes very considerably progress for
professionals in this field; [0022] digitizing documents produced
by artists, e.g. using airbrushes or other tools in association
with inks or pigments that are difficult to bring within the RGB
gamut; [0023] in science, measuring the colors of test strips or
solutions in laboratory applications; [0024] in the textile field,
digitizing samples: textiles are printed using dyes that present a
gamut that is extremely large; [0025] calorimetric inspection in
production lines, e.g. in the field of printing, to verify at the
outlet from a printing machine that samples of a document as
actually printed do indeed comply with the original color selection
delivered to the printer; and [0026] in the field of illustration
or photography, reproducing documents containing subtle shades of
color, such as water colors, or old books, in which the
illustrations were made using specific stains or inks that can be
reproduced faithfully only by using multispectral digitizing.
[0027] The application of the invention to a multispectral
single-pass flat-bed scanner is nevertheless not limiting, and it
will be understood that the invention can be applied to other types
of scanner, for example to digital photographic systems or to
systems such as that described in WO-A-00/25509 (Lumiere Technology
SA) where a photosensitive sensor scans an image plane formed by a
lens of an analysis chamber, the article under observation itself
being illuminated by a narrow strip of light that is moved
synchronously with the scanning of the sensor.
[0028] The neural network of the spectral reconstruction means of
the invention is preferably a network having multiple thresholds,
suitable for receiving as inputs the N measurement values, for
applying specific weightings to the N values, and for outputting a
plurality of individual reconstituted quantized values associated
with corresponding spectral components of the reflectance of the
pixel. Under such circumstances, the neural network may output a
number N' of individual reconstituted quantized values that is
greater than the number N of measurement values, in particular a
number N' of at least 15 values, preferably of at least 25 values,
and more preferably 30 values, for a number N of measurement values
that is equal to 6 or to 7.
[0029] There follows a description of an embodiment of the
invention given with reference to the accompanying drawings.
[0030] FIG. 1 is a diagrammatic view showing the configuration of
the various mechanical components of a single-pass flat-bed
scanner.
[0031] FIG. 2 shows the principle of spectrum reconstruction by
means of a neural network.
[0032] FIG. 3 is a view of an integrated multispectral CCD sensor,
which view is enlarged in part to show the series of associated
filters.
[0033] FIG. 4 is a graph showing the transmittance curves of the
various filters of the FIG. 3 sensor.
[0034] FIG. 5 shows the chromaticity diagram in the CIE system,
showing the respective gamuts of different colorimetry analysis
systems, relative to the extent of the visible color space.
[0035] FIG. 1 shows a general structure of a multispectral flat-bed
scanner to which the invention can advantageously be applied.
[0036] As mentioned above, this type of scanner is not limiting and
the invention can be implemented with other analyzer devices, for
example the document reproduction chamber described in
WO-A-00/25509, where an image of the document is formed on an image
plane that is scanned by a sensor driven by a micrometer
system.
[0037] The mechanism of a single-pass flat-bed scanner, e.g. of the
A4 or A3 office scanner type, is itself well known.
[0038] The scanner 10 serves to scan a document 12 laid flat
against a stationary scanner window 14. First moving equipment 16
carries lighting means 18 suitable for illuminating a narrow
transverse strip of the document 12. The equipment 16 is movable in
linear translation in a direction perpendicular to the illuminated
scan line, and an optical assembly is provided for forming an image
of said line on a stationary linear sensor 20 via mirrors 22, 24,
and 26, and via a lens 28. The mirror 22 is secured to the moving
equipment 16, while the mirrors 24 and 26 are mounted on other
moving equipment 30 that is adjustable in position, and the lens 28
is mounted on a support 32 that is movable so as to vary the
optical magnification factor.
[0039] The sensor 20 is a multispectral sensor that typically
delivers six color signals in six distinct bands.
[0040] Nevertheless, the invention is not limited to this number
(six) of bands; that merely corresponds to the best compromise at
present. It should merely be understood that the number of bands is
greater than the three bands of RGB sensors, that present
shortcomings as set out above, and less than the twelve or thirteen
bands of the complex apparatuses mentioned above and used for
example in the field of museography, which, because of their
complexity, cannot be implemented in simple manner, in particular
in the form of a single-pass scanner. The use of a number of
filters less than six, for example five filters or even only four
filters, comes within the ambit of the invention, but will
naturally give a result that is qualitatively inferior.
[0041] The problem of the invention essentially consists in
reconstituting a reflectance spectrum from these six values, and
thus in calculating intermediate values (an operation referred to
as "reconstruction"), while minimizing the interpolation noise
added by the operation.
[0042] This is a known problem as numerous proposals have been made
for solving it.
[0043] These proposals can be classified in three main methods.
[0044] A first method referred to as "direct reconstruction"
consists in characterizing all of the elements of the image
acquisition and digitizing system: a spectrum curve for the
lighting device, the spectral sensitivity specific to the sensor,
the respective transmittances of the filters used, and the
transmittances of the various components in the optical system.
Once the acquisition system has been characterized in this way, it
is then possible to construct a direct link between the stimulation
and the response of the system in the form of a matrix operator
having K rows by N columns, where K is the number of filters used
by the system and N is the number of samples that result from the
digital quantization.
[0045] Nevertheless, that direct method presents the drawback of
requiring prior characterization of each component of the system,
implying a certain amount of experimentation (measuring the
spectral sensitivity of the sensor using a monochromator, measuring
the transmittance of the optical system and of the filters with a
spectrophotometer, etc.). It is also very sensitive to problems
with electrical noise, which appears to be difficult to quantize as
such. For these reasons, it is at present of interest above all on
theoretical grounds and has not given rise to concrete utilizations
other than in experimental applications.
[0046] A second method, referred to as "reconstruction by
interpolation", involves solely the response of the camera to a
perfect white reference. After normalization relative to the
standard white, the camera is considered as a spectrum sampler, a
point of the spectrum curve being measured once every 40 nanometers
(nm) in the visible range, for example. Intermediate points of the
spectrum are then reconstructed by an interpolation method, e.g. by
a cubic spline method or by a modified discrete sine transform
(MDST), so as to obtain a spectrum that is reconstructed at points
that are spaced apart, for example, by 10 nm, 5 nm, or 1 nm.
[0047] That interpolation method presents the advantage of
requiring knowledge only of the response of the camera, using
digital processing on the basis of conventional algorithms.
Nevertheless, it assumes that the spectrum to be reconstructed is a
spectrum presenting a profile that is relatively smooth; the
algorithms used for reconstructing the missing points are incapable
of detecting a narrow peak in the spectrum, which peak will be
smoothed out and the reconstituted information will be deformed. In
addition, a large amount of interpolation noise becomes superposed
thereon, and that degrades the performance of the method very
quickly.
[0048] In practice, spectrum analysis must be capable of being used
on spectra that are complex, for example those of pigments used in
painting and presenting a spectrum profile that is highly
particular, such that if it is smoothed by the reconstruction
algorithm it will be immediately perceived as being deformed by an
observer trained to distinguish between subtle shades of color and
substitutions by means of a color that is close. In addition,
implementing that technique with an acceptable degree of fidelity
in reproducing colors implies a relatively large number of filters
in order to obtain sufficient starting samples, typically eleven or
thirteen filters, which restricts its use to cameras that are
relatively complex and does not enable it to be implemented in the
form of a mass-produced scanner, e.g. having a color analysis
system relying on six bands only.
[0049] The third method, to which the present invention belongs, is
known as "indirect reconstruction" or "reconstruction by training".
Essentially, this method uses a standardized color chart that makes
it possible, by extrapolation, to model a transfer function between
reference spectra as measured on the chart for each of the samples,
and the corresponding responses of the camera.
[0050] As explained below, the invention proposes a certain number
of improvements to that known method of indirect reconstruction in
order to be able to determine the looked-for transfer function with
performance that is much better than that which it has been
possible to provide in the past, and also making it possible to
implement the method on the basis of information delivered by a
sensor that analyzes the spectrum over a small number of bands,
typically only six bands (where six is a value that is typical, but
naturally not limiting).
[0051] The implementation of the method by the invention is shown
diagrammatically in FIG. 2.
[0052] The sensor 20 of the scanner is typically constituted by a
six-filter sensor, as mentioned above, and it therefore delivers
six quantized color-measurement values for each pixel. These values
are applied to a neural network 40 having six inputs and thirty
outputs (assuming that it is desired to reconstruct the spectral
reflectance over thirty points). The neural network 40 is
associated with a memory 42 that stores a knowledge base made up of
known spectral reflectance values for a certain number of reference
samples, advantageously samples selected as a function of the
intended application: for example, in applications in the field of
museography or of illustration, a database built up from the 300
main pigments used in painting. This knowledge base serves to
determine the various weightings applied by the neural network.
[0053] The neural network 40 may optionally be made in the form of
a specific digital signal processor integrated in the multispectral
sensor 20.
[0054] The sensor 20 used for implementing the invention is
advantageously an integrated sensor of the kind shown in FIG. 3, in
the form of a strip having six (or possibly seven) lines of
photo-sensitive sites, e.g. 10,000 or 12,000 photo-sensitive sites
each, with each of the lines being associated with a corresponding
filter 51 to 56 with mass coloration. The respective spectral
responses 61 to 66 of these filters are shown in FIG. 4.
[0055] The multispectral sensor 20 is combined with the mechanical
and optical scanning system of the scanner in the same manner as a
conventional three-color sensor of the prior art, thus making it
possible to deliver simultaneously for each pixel of the line of
the document being scanned a series of 6.times.12 bits (or
7.times.12 bits) constituting the quantized measurement values that
are applied to the neural network 40.
[0056] The invention makes it possible with a sensor having only
six filters to obtain a gamut covering the entire visible range,
enabling very subtle color shades to be reproduced with very great
fidelity, with performance that is much better than that which it
has been possible to offer in the past with six-color analysis
systems or a fortiori with three-color systems.
[0057] FIG. 5 is thus a chromaticity diagram in the CIE colorimetry
system showing the visible range V (which can be covered in full by
a scanner of the invention) and relative thereto the respective
restricted gamuts obtained using CMY and RGB three-color analyses,
or using six-color RGBCMY analysis.
[0058] Furthermore, the sensor of the invention is easy to
integrate in a mass-produced scanner of the single-pass type, and
produces results that are equivalent to those which until now have
required the use of a complex apparatus with eleven or thirteen
filters, and requiring as many analysis passes.
[0059] There follows a more detailed description of the manner in
which the samples picked up by the sensor are processed in order to
achieve such results.
Principle of Indirect Multispectrum Reconstruction
[0060] The starting point of this method consists in using a
multispectral camera to form an image of a chart having P samples
(e.g. P=250 or 300 samples) of reference colors that are
representative of the documents that are to be scanned, and that
have spectral reflectance curves that are known accurately, e.g.
previously determined by means of a spectrophotometer.
[0061] For each sample, a vector c.sub.p of dimension K is obtained
containing the responses of the camera in the various bands that
are analyzed (where K is the number of filters used in the
acquisition system, typically K=6 or 7), and an associated vector
r.sub.p of dimension N representative of the associated spectral
reflectance, previously determined by means of a spectrophotometer
(where N is the number of measured spectrum points, typically N=30
points).
[0062] The problem consists in discovering from the data as
determined in this way (the starting reference data and the
corresponding responses of the camera), the corresponding transfer
function which is a matrix operator Q of dimension N.times.K such
that: R=QC where R is a matrix of dimensions N.times.P of the
vectors r.sub.p, and C is a matrix of dimension K.times.P of the
vectors c.sub.p. It can be shown that this expression can be
rewritten as follows: Q=RC.sup.t(CC.sup.t).sup.-1 which can be
expressed in the following form: Q=R pinv(C) where the notation
pinv(C) designates the pseudo-inverse of the matrix C, i.e.:
pinv(C)=C.sup.t(CC.sup.t).sup.-1 This is a matrix that is easily
calculated using algorithms that are themselves known. Optimization
by Applying a Bootstrap Method
[0063] In a first aspect of the invention, the starting data used
in implementing the indirect reconstruction is subjected to
statistical processing of the bootstrap type.
[0064] The bootstrap method is itself known, e.g. from Bootstrap
methods: another look at the jackknife, by B. Efron in Annals of
Statistics, 7, pp. 1-26, 1979. It is a computer resampling
technique serving to measure the accuracy of statistical estimates
by providing confidence intervals on the estimate of a statistical
population. To do this, resampling the data makes it possible to
incorporate by statistical inference information that is contained
in data associated with its probabilistic distribution.
[0065] The starting point of the invention consists in using this
statistical bootstrap processing technique for processing color
signals in order to improve the reconstruction of a spectrum
reference.
[0066] For this purpose, the above-defined matrices R and C are
resampled by randomly selecting their columns, using a uniform
probabilistic distribution for this selection.
[0067] This operation (a function written below as resample(.))
consists in producing from a given matrix another matrix that
comprises the columns as resampled in random manner. In the
resulting matrix, there will therefore be columns that are
repeated, and conversely, some of the columns of the original
matrix will no longer be found in the resampled version.
[0068] To implement the invention, the proposed algorithm forms a
reconstruction operator Q from matrices obtained by resampling the
above-defined matrices R and C, and it evaluates the distance
between the initial operator Q and the resulting operator Q.
[0069] A large number of operators Q are calculated in this way,
together with their respective distances from a test data set
R.sub.test and C.sub.test, after which the algorithm selects as the
final result the operator that presents the shortest distance (in
the least squares sense).
[0070] The algorithm can be expressed in pseudo-code as follows:
[0071] For i=1 . . . I [0072] R.sub.i=resample(R) [0073]
C.sub.i=resample(C) [0074] Q.sub.i=R.sub.i pinv(C.sub.i) [0075]
error.sub.i=.parallel.Q.sub.iC.sub.test-R.sub.test.parallel..sup.2
[0076] End For [0077] Select Q.sub.i having the smallest
error.sub.i where I is the number of iterations.
[0078] The function resample(.) transforms R and C in the same
manner with the same random selection on each iteration so that
both matrices contain columns that correspond.
[0079] In an optimized variant of this bootstrap method, the
selection is performed in non-random manner, in order to increase
the accuracy of the method and achieve convergence on the final
operator that is faster.
[0080] This improvement implements colorimetric video acquisition
performed concurrently with analysis of the reference color sample
chart.
[0081] If a lighting source is used in combination with appropriate
filters enabling the standard observer to be associated with a
standardized illuminance, it is possible under such circumstances
to emulate the behavior of a colorimeter and to obtain accurately
the chromatic coordinates in a single system.
[0082] This chromatic data can advantageously be delivered by a
secondary sensor directly integrated in the scanner, and delivering
information simultaneously with the scanning of the document.
[0083] The colorimetric video acquisition serves to locate the
greatest color differences between the response of the camera and
the corresponding chromatic coordinates. This knowledge of the
greatest differences can then be used for introducing favorable
bias when selecting which samples to eliminate in the bootstrap
algorithm so as to improve its effectiveness by concentrating its
effect on those samples of the chart that require the most
processing in order to optimize the transfer function that is to be
determined.
Using a Neural Network
[0084] In another aspect of the invention, multispectral
reconstruction by training is implemented by means of a neural
network.
[0085] This aspect of the invention is preferably provided in
combination with the above-explained bootstrap processing which
constitutes a statistical engine that is advantageously applicable
to the samples before they are applied to the neural network.
[0086] Nevertheless, these are two techniques that are distinct and
that can be used independently of each other, even though when used
in combination they naturally produce results that are particularly
advantageous.
[0087] Neural networks are generally defined as being networks
comprising a very large number of simple processors (neurons) that
are connected together by communication paths (connections)
conveying digital data encoded in various manners, the neurons
operating only on the inputs applied via their respective
connections.
[0088] Neural networks can be represented in the form of a matrix
having N inputs and N' outputs, with each of the output values
being dependent on the set of N input values as a function of
weightings allocated to each neuron. Individual neurons are
organized in subgroups each performing independent processing with
the result being forwarded to the following subgroup: information
thus propagates through the neural network, with the option of
applying output values to preceding subgroups
(back-propagation).
[0089] The weightings of the connections of the neurons are
adjusted by a data set determined on the basis of prior training.
The knowledge of the network (training) is thus stored in the
various weights, which are capable of adapting during processing.
The neural network subsequently presents behavior that takes
account of the parameters input during the training stage, thus
making it suitable for implementing a certain kind of
generalization on the basis of particular cases.
[0090] A detailed study of this concept can be found in particular
in Mixture density network by C. M. Bishop in Neural Computing
Research Group Report NCRG/4288, Aston University, United Kingdom,
1996.
[0091] In the invention, where it is desired to perform
multispectral reconstruction, the training stage consists in
acquiring the multiple samples of the reference color sample chart
(typically 250 to 300 samples) and in storing the data in a
knowledge base containing the corresponding weights for all of the
neurons in the network. The behavior of the network thus integrates
the knowledge of the spectral characteristics of the samples in the
chart.
* * * * *